Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review

• URL: http://arxiv.org/abs/2012.06310v1
• Date: Wed, 25 Nov 2020 07:02:14 GMT
• Title: Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review
• Authors: Parsa Sarosh, Shabir A. Parah, Romany F Mansur, G. M. Bhat
• Abstract summary: The emergence of COVID-19 has necessitated many efforts by the scientific community for its proper management. The use of Deep Learning (DL) and Artificial Intelligence (AI) can be sought in all of the above-mentioned spheres. It can be evaluated that DL and AI can be effectively implemented to withstand the challenges posed by the global emergency.
• Score: 5.237999056930947
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The emergence of COVID-19 has necessitated many efforts by the scientific community for its proper management. An urgent clinical reaction is required in the face of the unending devastation being caused by the pandemic. These efforts include technological innovations for improvement in screening, treatment, vaccine development, contact tracing and, survival prediction. The use of Deep Learning (DL) and Artificial Intelligence (AI) can be sought in all of the above-mentioned spheres. This paper aims to review the role of Deep Learning and Artificial intelligence in various aspects of the overall COVID-19 management and particularly for COVID-19 detection and classification. The DL models are developed to analyze clinical modalities like CT scans and X-Ray images of patients and predict their pathological condition. A DL model aims to detect the COVID-19 pneumonia, classify and distinguish between COVID-19, Community-Acquired Pneumonia (CAP), Viral and Bacterial pneumonia, and normal conditions. Furthermore, sophisticated models can be built to segment the affected area in the lungs and quantify the infection volume for a better understanding of the extent of damage. Many models have been developed either independently or with the help of pre-trained models like VGG19, ResNet50, and AlexNet leveraging the concept of transfer learning. Apart from model development, data preprocessing and augmentation are also performed to cope with the challenge of insufficient data samples often encountered in medical applications. It can be evaluated that DL and AI can be effectively implemented to withstand the challenges posed by the global emergency

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